library(glatos)
library(sf)
library(mapview)
library(plotly)
library(tidyverse)
det_path <- file.path("..", "data", "detections.csv")
detection_events <-
read_glatos_detections(det_path) %>%
false_detections(tf = 3600) %>%
filter(passed_filter != FALSE) %>%
detection_events(location_col = 'station')
## The filter identified 93 (1.3%) of 7180 detections as potentially false.
## The event filter distilled 7087 detections down to 3537 distinct detection events.
rx_file <- file.path("..", "data", "deployments.csv")
receivers <-
read_glatos_receivers(rx_file)
## Combine detection and reciever datasets into a single combined data frame
combined_data <-
detection_events %>%
left_join(receivers, by = c("location" = "station")) %>%
filter(first_detection >= deploy_date_time, first_detection <= recover_date_time)
head(combined_data)
## event animal_id location mean_latitude mean_longitude
## 1 1 153 TTB-002 43.39165 -83.99264
## 2 2 153 TTB-001 43.38709 -83.98737
## 3 3 153 TTB-002 43.39165 -83.99264
## 4 4 153 TTB-001 43.38709 -83.98737
## 5 5 153 TTB-002 43.39165 -83.99264
## 6 6 153 TTB-001 43.38709 -83.98737
## first_detection last_detection num_detections res_time_sec
## 1 2012-04-29 01:48:37 2012-04-29 02:05:33 8 1016
## 2 2012-04-29 02:08:00 2012-04-29 02:08:00 1 0
## 3 2012-04-29 02:08:00 2012-04-29 02:08:00 1 0
## 4 2012-04-29 02:09:50 2012-04-29 02:09:50 1 0
## 5 2012-04-29 02:09:50 2012-04-29 02:09:50 1 0
## 6 2012-04-29 02:12:24 2012-04-29 02:12:24 1 0
## glatos_array station_no consecutive_deploy_no intend_lat intend_long
## 1 TTB 2 3 NA NA
## 2 TTB 1 3 NA NA
## 3 TTB 2 3 NA NA
## 4 TTB 1 3 NA NA
## 5 TTB 2 3 NA NA
## 6 TTB 1 3 NA NA
## deploy_lat deploy_long recover_lat recover_long deploy_date_time
## 1 43.39165 -83.99264 NA NA 2012-03-14 16:50:00
## 2 43.38709 -83.98737 NA NA 2012-03-14 16:35:00
## 3 43.39165 -83.99264 NA NA 2012-03-14 16:50:00
## 4 43.38709 -83.98737 NA NA 2012-03-14 16:35:00
## 5 43.39165 -83.99264 NA NA 2012-03-14 16:50:00
## 6 43.38709 -83.98737 NA NA 2012-03-14 16:35:00
## recover_date_time bottom_depth riser_length instrument_depth
## 1 2012-07-17 17:04:00 NA NA NA
## 2 2012-07-17 16:20:00 NA NA NA
## 3 2012-07-17 17:04:00 NA NA NA
## 4 2012-07-17 16:20:00 NA NA NA
## 5 2012-07-17 17:04:00 NA NA NA
## 6 2012-07-17 16:20:00 NA NA NA
## ins_model_no glatos_ins_frequency ins_serial_no deployed_by comments
## 1 VR2W 69 113213
## 2 VR2W 69 119331
## 3 VR2W 69 113213
## 4 VR2W 69 119331
## 5 VR2W 69 113213
## 6 VR2W 69 119331
## glatos_seasonal glatos_project glatos_vps
## 1 YES HECWL NO
## 2 YES HECWL NO
## 3 YES HECWL NO
## 4 YES HECWL NO
## 5 YES HECWL NO
## 6 YES HECWL NO
## Plot your combined dataset
combined_sf_data <- combined_data %>%
group_by(animal_id, location, deploy_lat, deploy_long) %>%
summarise(Num.Det = n()) %>%
st_as_sf(coords = c("deploy_long", "deploy_lat"), crs = 4326) %>%
mapview(zcol = "animal_id", cex = "Num.Det", burst = T, legend = F)
combined_sf_data
library(gganimate)
library(ggmap)
library(lubridate)
plot_data <-
combined_data %>%
mutate(timestep = round_date(first_detection, unit = "1 days")) %>%
group_by(timestep, animal_id) %>%
summarise(lon = mean(deploy_long),
lat = mean(deploy_lat))
base <-
get_stamenmap(
bbox = c(left = min(plot_data$lon),
bottom = min(plot_data$lat),
right = max(plot_data$lon),
top = max(plot_data$lat)),
maptype = "toner-lite",
crop = F,
zoom = 8)
walleye.plot <-
ggmap(base) +
geom_point(data = plot_data, aes(x = lon, y = lat, group = animal_id, color = animal_id), size = 2) +
geom_path(data = plot_data, aes(x = lon, y = lat, group = animal_id, color = animal_id)) +
labs(title = "Walleye animation",
x = "Longitude", y = "Latitude", color = "Tag ID")
walleye.plot
ggplotly(walleye.plot)
walleye.animation <-
walleye.plot +
labs(subtitle = 'Date: {format(frame_along, "%d %b %Y")}') +
transition_reveal(timestep) +
shadow_mark(past = T, future = F)